Design & build predictive analytics capabilities into industrial equipment
to avoid downtime, improve productivity and quality
Trusted by 150+ industrial enterprises across US, Canada, UK, Australia & Germany
Machine performance or condition monitoring solutions remain largely reactive in nature until you build in predictive capabilities that can help forecast failures, throughput, defects etc. This needs augmenting historical machine, process, and other environmental data, modeling with the right algorithms, deploying and tuning it to generate accurate predictions.
At Saviant, our machine learning consulting and implementation teams help smart instruments and machines manufacturers to build custom predictive analytics solutions, with real-time & historical data. Their solutions are equipped with predictive & AI capabilities, to enable fault-free operations for business continuity of their Enterprise clients.
For example, their predictive analytics solutions can now:
through timely alerts and alarms across operations, thereby preventing downtime events or failures.
using the identified patterns and correlations from real-time & historical data of machine and operations.
by integrating predictive analytics solutions with customer’s real-time systems.
as well as anomalies on the web and mobile apps to take corrective actions in real-time.
The solution helped reduce machine downtime and enabled industrial equipment and systems to be more intelligent about their availability, operating efficiency and failures.
and start downloading the case study
Our predictive analytics consulting & implementation teams follow a particular approach to build custom solutions and help achieve your goals, which includes:
Assessing what’s the data you are capturing, existing data sources, your objective to build a predictive analytics solution, and determining what additional data is needed to create the use case.
Extracting, transforming, and labeling the required data. Shortlisting candidate ML models to build, test & evaluate them for feature engineering and hyper-parameter tuning.
Analyzing the results post-processing and comparing models’ behavior & hypothesis. Deploying Model as a Service and continually monitoring/retrain it to stay relevant and accurate.
Integrating the solution seamlessly with the existing system after the successful PoC implementation. Running it to check if the desired output has been achieved.
Saviant is one of the trusted predictive analytics consulting firms to deliver enterprise-grade solutions to customers worldwide. We help them with solutions around predictive maintenance, forecasting, and process optimizations using expertise in machine learning and analytics on Cloud platforms like Microsoft Azure.